02 · Integration

Enterprise Data Integration

Source-to-target integration with defined field mappings, transformation rules and validation controls.

Proposed extension stack PythonEnterprise LLMSharePoint

Case scope: The enterprise process, systems and control foundation reflects hands-on delivery patterns. AI and agent components shown below are proposed design extensions.

Mapping analysisIntegration checks Field definitionsMapping history

At a Glance

Business problemSource and target systems use different field names, structures and rules, creating mapping ambiguity and downstream integration risk

My contribution Integration requirements · field mapping · XML/API validation · UAT

Current environmentSAP exports · source/target schemas · mapping specifications · SharePoint

Business valueField mappings are validated, ambiguous matches are reviewed and mapping decisions are recorded.

Proposed AI extension · AI / Agent / Human

Helps resolve unclear source and target field definitions and suggests mappings when the match is not obvious.

Agent

Compares schemas, surfaces mappings that need review and records approved mapping decisions for future reference.

Human

Integration and business SMEs approve uncertain mappings and transformations. Overrides and reasons are captured.

Current workflow

01Source analysis

02Field mapping

03Transform rules

04Validation

05Target handoff

What I owned

Mapping requirementsSource/target definitions Business field meaningTransformation rules Validation criteriaException criteria Acceptance criteriaUAT AI use-case definitionAI output requirements

Proposed AI-enabled architecture

SAP export→ SharePoint→ Python checks→ Enterprise LLM→ Review queue→ Human approval→ Mapping logged

Technical design

MVP scenarios

Scenario 1Exact match. Source and target fields match clearly with no ambiguity.Pass

Scenario 2Known transformation. Field requires a predefined transformation rule. Handled deterministically.Deterministic

Scenario 3Ambiguous mapping. Multiple possible target fields exist. AI recommends the most likely match with reasoning and routes for review.Review

Scenario 4No target equivalent. No valid match exists. AI identifies the closest candidates or flags when no suitable mapping is evident and routes the decision for review.Review

Controls and governance

Exact matches and hard validation rules remain deterministic.

AI only evaluates unresolved or ambiguous cases.

AI does not create mappings without human confirmation.

Human approval is required for all uncertain mappings.

Overrides and reasons are logged.

Missing context triggers a review request, not a guess.

Implementation note

Azure OpenAI is the proposed LLM for evaluating ambiguous source-to-target mappings.